Evidence map›Paper›PMID 29157126›Full record

ArticleJournal of biopharmaceutical statistics2018

Sample size calculations for blinding assessment.

Victoria Landsman, Mark Fillery, Howard Vernon, Heejung Bang

Abstract read
In one paragraph

Article in Journal of biopharmaceutical statistics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Victoria Landsmana Division of Biostatistics , Institute for Work and Health , Toronto, Ontario, Canada.
Mark Filleryc Department of Research , Canadian Memorial Chiropractic College , Toronto , Ontario , Canada.
Howard Vernonc Department of Research , Canadian Memorial Chiropractic College , Toronto , Ontario , Canada.
Heejung Bangd Division of Biostatistics, Department of Public Health Sciences , University of California , Davis , CA , USA.

Funding

UC Davis Clinical and Translational Science CenterUL1TR001860 · NCATS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI KENYON, NICHOLAS J., LYLES, COURTNEY REES · 2016 to 2025
$47.3M
Sex Differences in response to an exercise for hyperkyphosisP50AR063043 · NIAMS · UNIVERSITY OF CALIFORNIA AT DAVIS · PI LANE, NANCY E · 2012 to 2017
$5.7M
Validation of a novel sham cervical spinal manipulation procedure.R21AT004396 · NCCIH · CANADIAN MEMORIAL CHIROPRACTIC COLLEGE · PI VERNON, HOWARD TERRY · 2008 to 2008
$156k
NCATS NIH HHS UL1 TR001860NCCIH NIH HHS R21 AT004396NIAMS NIH HHS P50 AR063043
6 · The paper itself

Abstract

Blinding is a critical component in randomized clinical trials along with treatment effect estimation and comparisons between the treatments. Various methods have been proposed for the statistical analyses of blinding-related data, but there is little guidance for determining the sample size for this type of data, especially if blinding assessment is done in pilot studies. In this paper, we try to fill this gap and provide simple methods to address sample size calculations for a "new" study with different research questions and scenarios. The proposed methods are framed in terms of estimation/precision or statistical testing to allow investigators to choose the best suited method for their goals. We illustrate the methods using worked examples with real data.

Indexed as

Data Interpretation, StatisticalDatabases, FactualDouble-Blind MethodHumansRandomized Controlled Trials as TopicSample SizeBlinding indexclinical trialcontingency tablemaskingpatient blinding

Identifiers

PMID29157126
PMCPMC5960641

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.